The pipeline consists of the following steps:
- Normal sample alignment using BWA-MEM.
- Tumor sample alignment using BWA-MEM.
- Variant calling with MuTect2.
The pipeline is run as a Databricks job. Most likely, a Databricks solutions architect will work with you to set up the initial job. The necessary details are:
- The cluster configuration should use Databricks Runtime for Genomics.
- The task should be the tumor/normal pipeline notebook found at the bottom of this page.
- For best performance, use compute optimized instances with at least 60GB of memory. We recommend c5.9xlarge.
- If you’re running base quality score recalibration, use general purpose (m5.4xlarge) instances instead since this operation requires more memory.
- To reduce costs, use all spot workers with the Spot fall back to On-demand option selected.
- Attach 1 500GB SSD EBS volume
The pipeline accepts parameters that control its behavior. The most important and commonly changed parameters are documented here. To view all available parameters and their usage information, run the first cell of the pipeline notebook. New parameters are added regularly. Parameters can be set for all runs or per-run.
|manifest||n/a||The manifest describing the input.|
|output||n/a||The path where pipeline output should be written.|
|exportVCF||false||If true, the pipeline writes results to a VCF file as well as Delta.|
|perSampleTimeout||12h||A timeout applied per sample. After reaching this timeout, the pipeline continues on to the next sample. The value of this parameter must include a timeout unit: ‘s’ for seconds, ‘m’ for minutes, or ‘h’ for hours. For example, ‘60m’ will result in a timeout of 60 minutes.|
To optimize runtime, set
spark.sql.shuffle.partitions in the Spark config to three times the number of cores of the cluster.
You must configure the reference genome using an environment variable. To use GRCh37, set an environment variable like this:
To use GRCh38, change
To use a custom reference genome, see instructions in Custom reference genomes.
Manifest blobs are supported in Databricks Runtime 6.6 for Genomics and above.
The manifest is a CSV file or blob describing where to find the input FASTQ or BAM files. An example:
pair_id,file_path,sample_id,label,paired_end,read_group_id HG001,*_R1_*.normal.fastq.bgz,HG001_normal,normal,1,read_group_normal HG001,*_R2_*.normal.fastq.bgz,HG001_normal,normal,2,read_group_normal HG001,*_R1_*.tumor.fastq.bgz,HG001_tumor,1,tumor,read_group_tumor HG001,*_R2_*.tumor.fastq.bgz,HG001_tumor,2,tumor,read_group_tumor
If your input consists of unaligned BAM files, you should omit the
pair_id,file_path,sample_id,label,paired_end,read_group_id HG001,*.normal.bam,HG001_normal,normal,,read_group_tumor HG001,*.tumor.bam,HG001_tumor,tumor,,read_group_normal
The tumor and normal samples for a given individual are grouped by the
pair_id field. The tumor and normal sample names read group names must be different within a pair.
If the provided manifest is a file, the
file_path field in each row may be an absolute path or a path relative to
the manifest file. If the provided manifest is a blob, the
file_path field must be an absolute path. You can
(*) to match many files.
The tumor/normal pipeline shares many operational details with the other Databricks pipelines. For more detailed usage information, such as output format structure, tips for running programmatically, steps for setting up custom reference genomes, and common issues, see DNASeq pipeline.